[House Hearing, 115 Congress]
[From the U.S. Government Publishing Office]


                        ARTIFICIAL INTELLIGENCE:
                         WITH GREAT POWER COMES
                          GREAT RESPONSIBILITY

=======================================================================

                             JOINT HEARING

                               BEFORE THE

               SUBCOMMITTEE ON RESEARCH AND TECHNOLOGY &
                         SUBCOMMITTEE ON ENERGY

              COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY
                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED FIFTEENTH CONGRESS

                             SECOND SESSION

                               __________

                             JUNE 26, 2018

                               __________

                           Serial No. 115-67

                               __________

 Printed for the use of the Committee on Science, Space, and Technology


       
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              COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY

                   HON. LAMAR S. SMITH, Texas, Chair
FRANK D. LUCAS, Oklahoma             EDDIE BERNICE JOHNSON, Texas
DANA ROHRABACHER, California         ZOE LOFGREN, California
MO BROOKS, Alabama                   DANIEL LIPINSKI, Illinois
RANDY HULTGREN, Illinois             SUZANNE BONAMICI, Oregon
BILL POSEY, Florida                  AMI BERA, California
THOMAS MASSIE, Kentucky              ELIZABETH H. ESTY, Connecticut
RANDY K. WEBER, Texas                MARC A. VEASEY, Texas
STEPHEN KNIGHT, California           DONALD S. BEYER, JR., Virginia
BRIAN BABIN, Texas                   JACKY ROSEN, Nevada
BARBARA COMSTOCK, Virginia           CONOR LAMB, Pennsylvania
BARRY LOUDERMILK, Georgia            JERRY McNERNEY, California
RALPH LEE ABRAHAM, Louisiana         ED PERLMUTTER, Colorado
GARY PALMER, Alabama                 PAUL TONKO, New York
DANIEL WEBSTER, Florida              BILL FOSTER, Illinois
ANDY BIGGS, Arizona                  MARK TAKANO, California
ROGER W. MARSHALL, Kansas            COLLEEN HANABUSA, Hawaii
NEAL P. DUNN, Florida                CHARLIE CRIST, Florida
CLAY HIGGINS, Louisiana
RALPH NORMAN, South Carolina
DEBBIE LESKO, Arizona
                                 ------                                

                Subcommittee on Research and Technology

                 HON. BARBARA COMSTOCK, Virginia, Chair
FRANK D. LUCAS, Oklahoma             DANIEL LIPINSKI, Illinois
RANDY HULTGREN, Illinois             ELIZABETH H. ESTY, Connecticut
STEPHEN KNIGHT, California           JACKY ROSEN, Nevada
BARRY LOUDERMILK, Georgia            SUZANNE BONAMICI, Oregon
DANIEL WEBSTER, Florida              AMI BERA, California
ROGER W. MARSHALL, Kansas            DONALD S. BEYER, JR., Virginia
DEBBIE LESKO, Arizona                EDDIE BERNICE JOHNSON, Texas
LAMAR S. SMITH, Texas
                                 ------                                

                         Subcommittee on Energy

                   HON. RANDY K. WEBER, Texas, Chair
DANA ROHRABACHER, California         MARC A. VEASEY, Texas, Ranking 
FRANK D. LUCAS, Oklahoma                 Member
MO BROOKS, Alabama                   ZOE LOFGREN, California
RANDY HULTGREN, Illinois             DANIEL LIPINSKI, Illinois
THOMAS MASSIE, Kentucky              JACKY ROSEN, Nevada
STEPHEN KNIGHT, California           JERRY McNERNEY, California
GARY PALMER, Alabama                 PAUL TONKO, New York
DANIEL WEBSTER, Florida              BILL FOSTER, Illinois
NEAL P. DUNN, Florida                MARK TAKANO, California
RALPH NORMAN, South Carolina         EDDIE BERNICE JOHNSON, Texas
LAMAR S. SMITH, Texas
                            
                            C O N T E N T S

                             June 26, 2018

                                                                   Page
Witness List.....................................................     2

Hearing Charter..................................................     3

                           Opening Statements

Statement by Representative Barbara Comstock, Chairwoman, 
  Subcommittee on Research and Technology, Committee on Science, 
  Space, and Technology, U.S. House of Representatives...........     4
    Written Statement............................................     6

Statement by Representative Daniel Lipinski, Ranking Member, 
  Subcommittee on Research and Technology, Committee on Science, 
  Space, and Technology, U.S. House of Representatives...........     8
    Written Statement............................................    10

Statement by Representative Lamar Smith, Chairman, Committee on 
  Science, Space, and Technology, U.S. House of Representatives..    12
    Written Statement............................................    13

Statement by Representative Marc A. Veasey, Ranking Member, 
  Subcommittee on Energy, Committee on Science, Space, and 
  Technology, U.S. House of Representatives......................    14
    Written Statement............................................    15

Statement by Representative Randy K. Weber, Chairman, 
  Subcommittee on Energy, Committee on Science, Space, and 
  Technology, U.S. House of Representatives......................    16
    Written Statement............................................    18

Written statement by Representative Eddie Bernice Johnson, 
  Ranking Member, Committee on Science, Space, and Technology, 
  U.S. House of Representatives..................................    21

                               Witnesses:

Dr. Tim Persons, Chief Scientist, U.S. Government Accountability 
  Office
    Oral Statement...............................................    22
    Written Statement............................................    25

Mr. Greg Brockman, Co-Founder and Chief Technology Officer, 
  OpenAI
    Oral Statement...............................................    40
    Written Statement............................................    42

Dr. Fei-Fei Li, Chairperson of the Board and Co-Founder, AI4ALL
    Oral Statement...............................................    50
    Written Statement............................................    52

Discussion.......................................................    59

             Appendix I: Answers to Post-Hearing Questions

Dr. Jaime Carbonell, Director, Language Technologies Institute, 
  and Allen Newell Professor, School of Computer Science, 
  Carnegie Mellon University.....................................    82

Dr. Tim Persons, Chief Scientist, U.S. Government Accountability 
  Office.........................................................    89

Mr. Greg Brockman, Co-Founder and Chief Technology Officer, 
  OpenAI.........................................................    97

Dr. Fei-Fei Li, Chairperson of the Board and Co-Founder, AI4ALL..   105

            Appendix II: Additional Material for the Record

Dr. Jaime Carbonell, Director, Language Technologies Institute, 
  and Allen Newell Professor, School of Computer Science, 
  Carnegie Mellon University, written statement..................   112

Document submitted by Representative Bill Foster, Subcommittee on 
  Research and Technology, Committee on Science, Space, and 
  Technology, U.S. House of Representatives......................   123

Document submitted by Representative Neal P. Dunn, Subcommittee 
  on Energy, Committee on Science, Space, and Technology, U.S. 
  House of Representatives.......................................   150

 
                        ARTIFICIAL INTELLIGENCE:
                         WITH GREAT POWER COMES
                          GREAT RESPONSIBILITY

                              ----------                              


                         TUESDAY, JUNE 26, 2018

                  House of Representatives,
        Subcommittee on Research and Technology and
                            Subcommittee on Energy,
               Committee on Science, Space, and Technology,
                                                   Washington, D.C.

    The Subcommittees met, pursuant to call, at 10:37 a.m., in 
Room 2318 of the Rayburn House Office Building, Hon. Barbara 
Comstock [Chairwoman of the Subcommittee on Research and 
Technology] presiding.

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    Chairwoman Comstock. The Committee on Space, Science, and 
Technology will come to order. Without objection, the Chair is 
authorized to declare recesses of the Committee at any time.
    Good morning, and welcome to today's hearing entitled 
``Artificial Intelligence--With Great Power Comes Great 
Responsibility.''
    I now recognize myself for five minutes for an opening 
statement.
    First, I would like to note that one of our witnesses, Dr. 
Jaime Carbonell from Carnegie Mellon University, is unable to 
be here today due to a medical emergency. We wish him well and 
a speedy recovery, and, without objection, we'll ensure his 
written testimony is made part of the hearing record.
    [The prepared statement of Mr. Carbonell appears in 
Appendix II]
    Chairwoman Comstock. One of the reasons I've been looking 
forward to today's hearing is to get a better sense from our 
witnesses about the nuances of the term artificial intelligence 
and implications for our society in a future where AI is 
ubiquitous.
    Of course, one might say AI is already pervasive. Since the 
term was first coined in the 1950s, we have made huge advances 
in the field of artificial narrow intelligence, which has been 
applied to many familiar everyday items such as the technology 
underlying Siri and Alexa.
    Called ANI for short, such systems are designed to conduct 
specific and usually limited tasks. For example, a machine that 
excels at playing poker wouldn't be able to parallel park a 
car. Conversely, AGI, or artificial general intelligence, 
refers to intelligent behavior across a range of cognitive 
tasks. If you enjoy science fiction movies, this definition may 
conjure up scenes from any number of classics such as Blade 
Runner, The Matrix, or The Terminator.
    For many individuals, the term AGI invokes images of robots 
or machines with human intelligence. As it turns out, we are 
decades away from realizing such AGI systems. Nevertheless, 
discussions about AGI and a future in which AGI is commonplace 
lead to some interesting questions worthy of analysis.
    For example, Elon Musk has been quoted as saying that AI, 
quote, ``is a fundamental risk to the existence of human 
civilization'' and poses ``vastly more risk'' than North Korea. 
Does that mean that AGI may evolve to a point one day when we 
will lose control over machines of our own creation? As 
farfetched as that sounds, minds and scientists are certainly 
discussing such questions.
    For the short term, however, my constituents are concerned 
about less existential issues that usually accompany new and 
evolving technologies, topics such as cybersecurity, protecting 
our privacy, and impacts to our nation's economy and to jobs.
    I am an original cosponsor of a bill introduced earlier 
this year titled the AI JOBS Act of 2018 to help our workplace 
prepare for the ways AI will shape the economy of the future. I 
will also introduce legislation today to reauthorize the 
National Institute of Standards and Technology, which includes 
language directing NIST to support development of artificial 
intelligence and data science.
    There is immense potential for AGI to help humans and to 
help our economy and all of the issues we're dealing with 
today, but that potential is also accompanied by some of the 
concerns that we will discuss today. I look forward to what our 
panel has to share with us about the bright as well as the 
challenging sides of the future with AGI.
    [The prepared statement of Chairwoman Comstock follows:]

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    Chairwoman Comstock. I now recognize the Ranking Member of 
the Research and Technology Subcommittee, the gentleman from 
Illinois, Mr. Lipinski, for his opening statement.
    Mr. Lipinski. Thank you, Chairwoman Comstock, and thank you 
to Chairman Weber for holding this hearing to understand the 
current state of artificial intelligence technology.
    Because of the rapid development of computational power, 
the capacity of AI to perform new and more complicated tasks is 
quickly advancing. Depending on who you ask, AI is the stuff of 
dreams or nightmares. I believe it is definitely the former, 
and I strongly fear that it could also be the latter.
    The science fiction fantasy worlds depicted on Hollywood's 
big and small screens alike capture imaginations about what the 
world might be like if humans and highly intelligent robots 
shared the Earth. Today's hearing is an opportunity to begin to 
understand the real issues in AI and to begin to move forward 
with informed science-based policymaking. This is a hearing 
that we may remember years from now hopefully as a bright 
beginning of a new era.
    Current AI technologies touch a broad scope of industries 
and sectors, including manufacturing, transportation, energy, 
health care, and many others. As we will hear from the 
witnesses today, artificial intelligence can be classified as 
artificial general intelligence or artificial narrow 
intelligence. From my understanding, it is applications of the 
latter such as machine learning that are underlying 
technologies that support some of the services and devices 
widely used by Americans today. These include virtual 
assistants such as Siri and Alexa, translation services such as 
Google Translate, and autonomous vehicle technologies. As the 
capabilities of AI improve, it will undoubtedly become a more 
essential part of our lives and our economy.
    While technology developers and industry look forward to 
making great strides in AI, I want to make sure my colleagues 
and I in Congress are asking the tough questions and carefully 
considering the most crucial roles that the federal government 
may have in shaping the future of AI. Federal investments in AI 
research are long-standing, and we must consider the 
appropriate balance and scope of federal involvement as we 
begin to better understand the various roles AI will play in 
our society.
    We are not starting from scratch in thinking about the 
appropriate role of the federal government in this arena. In 
2016, the White House issued the National Artificial 
Intelligence Research and Development Strategic Plan that 
outlines seven priorities for federally funded AI research. 
These included making long-term investments in AI, developing 
effective methods for human AI collaboration, and addressing 
the ethical, legal, and societal implications of AI, additional 
issues to address our safety and security, public data sets, 
standards, and workforce needs.
    Earlier this year, the Government Accountability Office 
issued a technology assessment report led by one of our 
witnesses, Dr. Persons, titled ``Artificial Intelligence: 
Emerging Opportunities, Challenges, and Implications.'' While 
noting significant potential for AI to improve many industries 
including finance, transportation, and cybersecurity, the 
report also noted areas where research is still needed, 
including how to optimally regulate AI, how to ensure the 
availability and use of high-quality data, understanding AI's 
effects on employment and education, and the development of 
computational ethics to guide the decisions made by software.
    These are all critical issues, but more and more I hear 
concern and widely varying predictions about AI's impact on 
jobs. AI has the potential to make some job functions safer and 
more efficient, but it also may replace others. We need to ask 
what are the long-term projections for the job market as AI 
grows? In this context, we also need to ask how well do our AI 
capabilities compare to those of other countries? What 
education, skills, and retraining will the workforce of the 
future need? These are very important questions as we think 
about ensuring a skilled workforce of the future that will help 
solidify U.S. leadership in AI as other countries vie for 
dominance in the field. If AI threatens some careers, it likely 
creates many others. We need to consider what Congress should 
do to shape this impact to make sure Americans are ready for it 
and make sure the benefits of AI are distributed widely.
    One other obvious issue of major concern when it comes to 
AI is ethics. There are many places where this becomes 
relevant. Currently, we need to grapple with issues regarding 
the data that are being used to educate machines. Biased data 
will lead to biased results from seemingly objective machines.
    A little further down the line are many difficult questions 
being raised in science fiction about a world of humans and 
intelligent robots. These are questions we will likely be 
called on to deal with in Congress, and we need to be ready.
    I want to thank all of our witnesses for being here today, 
and I look forward to your testimony. I'll yield back.
    [The prepared statement of Mr. Lipinski follows:]

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    Chairwoman Comstock. Thank you, Mr. Lipinski.
    And I now recognize the Chairman of the Energy 
Subcommittee, the gentleman from Texas, Mr. Weber, for his 
opening statement.
    Mr. Weber. Madam Chair, can I defer to the Chairman of the 
full Committee for his statement?
    Chairwoman Comstock. Yes, you may.
    Mr. Weber. Thank you.
    Chairman Smith. Thank you, Madam Chair. Thank you, Mr. 
Chairman. I didn't know you were going to do that.
    Madam Chair, often unknown to us, advances in artificial 
intelligence, or AI, touch many aspects of our lives. In the 
area of cybersecurity, AI reduces our reaction times to 
security threats. In the field of agriculture, AI monitors soil 
moisture and targets crop watering. And in the transportation 
lane, AI steers self-driving cars and manages intelligent 
traffic systems. Multiple technical disciplines, including 
quantum computing science, converge to form AI.
    Tomorrow, the Science Committee will mark up the National 
Quantum Initiative Act, which establishes a federal program to 
accelerate quantum research and development. This is a 
bipartisan bill that Ranking Member Eddie Bernice Johnson and I 
and others will introduce today. My hope is that every member 
of the committee will sponsor it or at least a majority.
    Transforming our current quantum research into real-world 
applications will create scientific and technological 
discoveries, especially in the field of artificial 
intelligence. These discoveries will stimulate economic growth 
and improve our global competitiveness, important 
considerations in light of China's advances in artificial 
intelligence and quantum computing. By some accounts, China is 
investing $7 billion in AI through 2030, and $10 billion in 
quantum research.
    The European Union has also issued a preliminary plan 
outlining a $24 billion public-private investment in AI between 
2018 and 2020. And Russian President Putin has noted that, 
quote, ``The leader in AI will rule the world,'' end quote. No 
doubt that's appealing to him. Yet, the Department of Defense's 
unclassified investment in AI was only $600 million in 2016, 
while federal spending on quantum totals only about $250 
million a year.
    The Committee will mark up a second piece of legislation to 
reauthorize the National Institute of Standards and Technology. 
The bill directs NIST to continue supporting the development of 
artificial intelligence and data science, including the 
development of machine learning and other artificial 
intelligence applications. It is simply vital to our nation's 
future that we accelerate our quantum computing and artificial 
intelligence efforts.
    Thank you, Madam Chair, and I yield back.
    [The prepared statement of Chairman Smith follows:]

[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]    

    Chairwoman Comstock. Thank you. And I now recognize the 
Ranking Member of the Energy Subcommittee, the gentleman from 
Texas, Mr. Veasey, for an opening statement.
    Mr. Veasey. I want to thank you, Chairwoman Comstock and 
Chairman Weber, for holding this hearing today, and thank you 
for all of the witnesses for providing expertise on this topic. 
I'm looking forward to hearing what everyone has to say today.
    America, of course, is a country of innovation, and in the 
digital world of today, more and more industries are relying on 
advanced technologies and connectivity to overcome new 
challenges. Artificial intelligence and big data are impacting 
every facet of production and commerce. AI has the ability to 
mimic cognitive functions such as problem-solving and learning, 
making it a critical resource as we encounter never-before-seen 
problems. Those in the energy sector have already seen 
improvements in productivity and efficiency and can expect to 
see even more advancement in the coming years.
    AI can be used to process and analyze data in previously 
unexplored ways. Technology such as sensor-equipped aircraft 
engines, locomotive, gas, and wind turbines are now able to 
track production efficiency and wear and tear on vital 
machinery.
    AI could also significantly improve our ability to detect 
failures before they occur and prevent disasters, saving money, 
time, and lives. And through the use of analytics, sensors, and 
operational data, AI can be used to manage, maintain, and 
optimize systems ranging from energy storage components to 
power plants to the electric grid. As digital technologies 
revolutionize the energy sector, we must ensure safe and 
responsible execution of these processes.
    AI systems can learn and adapt through continuous modeling 
of interaction and data feedback. Production must be put in 
place to guarantee the integrity of these mechanisms as they 
evaluate mass quantities of machine and user data. With 
Americans' right to privacy under threat, security of these 
connected systems is of the utmost importance.
    Nevertheless, I'm excited to learn about the valuable 
benefits that AI may be able to provide for our economy and our 
well-being alike. With a Gartner research study reporting that 
AI will generate 2.3 million jobs by 2020, that's a lot of 
jobs. The growth AI will bring not only to the energy sector 
but to health care, transportation, education, and so many 
others will help ensure the prosperity of our nation.
    I look forward to seeing what light our witnesses can shed 
on these topics and what we can do in Congress to help enable 
the development and deployment of these promising technologies.
    Madam Chairwoman, I yield back the balance of my time.
    [The prepared statement of Mr. Veasey follows:]

[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]    

    Chairwoman Comstock. Thank you. And I now recognize Mr. 
Weber for his opening statement.
    Mr. Weber. Thank you, Madam Chair.
    Today, we will hear from a panel of experts on next-
generation artificial intelligence, AI as we've all heard it 
described. And while some have raised concerns about the 
negative consequences of AI, this technology has the potential 
to solve fundamental science problems and improve everyday 
life. In fact, it's likely that everyone in this room benefits 
from artificial intelligence. For example, users of voice 
assistants, online purchase prediction, fraud detection that 
the gentleman from Texas mentioned, and music recommendation 
services are already utilizing aspects of this technology in 
their day-to-day life.
    In the past few years, the use of AI technology has rapidly 
expanded due to the increase in the volume of data worldwide, 
and to the proliferation of advanced computing hardware that 
allows for the powerful parallel processing of this data. The 
field of AI has broadened to include other advanced computing 
disciplines such as machine learning. We've heard about neural 
networks, deep learning computer vision, and natural language 
processing, just to name a few. These learning techniques are 
key to the development of AI technologies and can be used to 
explore complex relationships and produce previously unseen 
results on unprecedented timescales.
    The Department of Energy, DOE, is the nation's largest 
federal supporter of basic research in the physical science, 
with expertise in big-data science, high-performance computing, 
advanced algorithms, and data analytics and is uniquely 
positioned to enable fundamental research in AI and machine 
learning.
    DOE's Office of Science Advanced Scientific Computing 
Research program, or ASCR as we call it, program develops next-
generation supercomputing systems that can achieve the 
computational power needed for this type of critical research. 
This includes the Department's newest and most powerful 
supercomputer called Summit, which just yesterday, just 
yesterday was ranked as the fastest computing system in the 
entire world.
    AI also has broad applications in the DOE mission space. In 
materials science, AI helps researchers speed the experimental 
process and discover new compounds faster than ever before. In 
high-energy physics, AI finds patterns in atomic and particle 
collisions previously unseen by scientists.
    In fusion energy research, AI modeling predicts plasma 
behavior that will assist in building tokamak reactors, making 
the best of our investments in space. Even in fossil fuel 
energy production, AI systems will optimize efficiency and 
predict needed maintenance at power-generating facilities. AI 
technology has the potential to improve computational science 
methods for any big-data problem, any big-data problem. And 
with the next generation of supercomputers, the exascale 
computing systems that DOE is expected to field by 2021, 
American researchers utilizing AI technology will be able to 
track even bigger challenges.
    We cannot afford to fall behind in this compelling area of 
research, and big investments in AI by China and Europe already 
threaten U.S. dominance in this field. With the immense 
potential for AI technology to answer fundamental scientific 
challenges, it's quite clear we should prioritize this 
research.
    We should maintain, I will add, American competitive edge 
and American exceptionalism. This will help us to do that.
    I want to thank our accomplished panel of witnesses for 
their testimony today, and I look forward to hearing what role 
Congress can play and should play in advancing this critical 
area of discovery science.
    And, Madam Chair, I yield back.
    [The prepared statement of Mr. Weber follows:]

[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
    
    [The prepared statement of Full Committee Ranking Member 
Eddie Bernice Johnson]
[GRAPHIC] [TIFF OMITTED] T0877.011

    Chairwoman Comstock. Thank you. And I will now introduce 
today's witnesses. Our first witness today is Dr. Tim Persons, 
Chief Scientist at the U.S. Government Accountability Office. 
He also serves as a Director for GAO's Center for Science, 
Technology, and Engineering. Dr. Persons received a Bachelor of 
Science in physics from James Madison University and a Master 
of Science in nuclear physics from Emory University. He also 
earned a Master of Science in computer science and Ph.D. in 
biomedical engineering, both from Wake Forest University.
    Next, we have Mr. Greg Brockman, our second witness, who is 
Cofounder and Chief Technology Officer of OpenAI, a nonprofit 
artificial intelligence research company. Mr. Brockman is an 
investor in over 30 startups and a board member of the Stellar 
digital currency system. He was previously the CTO of Stripe, a 
payments startup now valued at over $9 billion. And he studied 
mathematics at Harvard and computer science at MIT.
    And our final witness is Dr. Fei-Fei Li, Chairperson of the 
Board and Cofounder of AI4ALL. In addition, Dr. Li is a 
Professor in the Computer Science Department at Stanford and 
the Director of the Stanford Artificial Intelligence Lab. In 
2017, Dr. Li also joined Google Cloud as Chief Scientist of AI 
and machine learning. Dr. Li received her Bachelor of Arts in 
physics from Princeton and her Ph.D. in electrical engineering 
from the California Institute of Technology.
    I now recognize Dr. Persons for five minutes to present his 
testimony.

                 TESTIMONY OF DR. TIM PERSONS,

                        CHIEF SCIENTIST,

             U.S. GOVERNMENT ACCOUNTABILITY OFFICE

    Dr. Persons. Good morning. Thank you, Chairwoman Comstock, 
Chairman Weber, Ranking Members Lipinski and Veasey and Members 
of the Subcommittee. I'm pleased to be here today to discuss 
GAO's technology assessment on artificial intelligence. To 
ensure the United States remains a leader in AI innovation, 
special attention will be needed for our education and training 
systems, regulatory structures, frameworks for privacy and 
civil liberties, and our understanding of risk management in 
general.
    AI holds substantial promise for improving human life, 
increasing the nation's economic competitiveness, and solving 
some of society's most pressing challenges. Yet, as a 
disruptive technology, AI poses risks that could have far-
reaching effects on, for example, the future labor force, 
economic inclusion, and privacy and civil liberties, among 
others.
    Today, I'll summarize three key insights arising from our 
recent work. First, the distinction between narrow versus 
general AI; second, the expected impact of AI on jobs, 
competitiveness, and workforce training; and third, the role 
the federal government can play in research, standards 
development, new regulatory approaches, and education.
    Regarding narrow versus general AI, narrow AI refers to 
applications that are task-specific such as tax preparation 
software, voice and face recognition systems, and algorithms 
that classify the content of images. General AI refers to a 
system exhibiting intelligence on par with or possibly 
exceeding that of humans. While science fiction has helped 
general AI capture our collective imaginations for some time, 
it is unlikely to be fully achieved for decades if at all. Even 
so, considerable progress has been made in developing narrow AI 
applications that outperform humans in specific tasks and are 
thus invoking crucially important economic policy and research 
considerations.
    Regarding jobs, competition, and the workforce, there is 
considerable uncertainty about the extent to which jobs will be 
displaced by AI and how many--how much any losses will be 
offset by job creation. In the near term, displacement to 
certain jobs such as call-center or retail workers may be 
particularly vulnerable to automation. However, in the long 
term, demand for skills that are complementary to AI is 
expected to increase, resulting in greater productivity. To 
better understand the impact of AI on employment moving 
forward, several experts underscored the need for new data and 
methods to enable greater insight into this issue.
    Regarding the role of the federal government, it will 
continue its crucial role in research and data-sharing, 
contributions to standards development, regulatory approaches, 
and education. One important research area of the federal 
government could support is enhancing the explainability of AI, 
which could help establish trust in the behavior of AI systems. 
The federal government could also incentivize data-sharing, 
including federal data that are subject to limitations for how 
they can be used, as well as creating frameworks for sharing 
data to improve the safety and security of AI systems. Such 
efforts may include supporting standards for explainability; 
data labeling and safety, including risk assessment; and 
benchmarking of AI performance against the status quo. It's 
always risk versus risk.
    Related to this, new regulatory approaches are needed, 
including the development of regulatory sandboxes for testing 
AI products, services, and business models, especially in 
industries like transportation, financial services, and health 
care. GAO's recent report on fintech found, for example, that 
regulators use sandboxes to gain insight into key questions, 
issues, and unexpected risks that may arise out of the emerging 
technologies. New rules governing intellectual property and 
data privacy may also be needed to manage the deployment of AI.
    Finally, education and training will need to be reimagined 
so workers have the skills needed to work with and alongside 
emerging AI technologies. For the United States to remain 
competitive globally and effectively manage AI systems, its 
workers will need a deeper understanding of probability and 
statistics across most if not all academic disciplines, that 
is, not just the physical, engineering, and biological 
sciences, as well as competency and ethics, algorithmic 
auditability, and risk management.
    In conclusion, the emergence of what some have called the 
fourth industrial revolution and AI's key role in driving it 
will require new frameworks for business models and value 
propositions for the public and private sectors alike. Even if 
AI technologies were to cease advancing today, no part of 
society or the economy would be directly or indirectly 
untouched by its transformative effects.
    I thank the committee leadership of the committees. Thanks 
to the members here for your holding a hearing on this very 
important topic today for such a time as this.
    Madam Chairwoman, Mr. Chairman, Ranking Members, this 
concludes my prepared remarks. I would be happy to respond to 
any questions that you or other Members of the Subcommittees 
have at this time.
    [The prepared statement of Dr. Persons follows:]

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    Chairwoman Comstock. Thank you. And I now recognize Mr. 
Brockman for five minutes.

                TESTIMONY OF MR. GREG BROCKMAN,

        CO-FOUNDER AND CHIEF TECHNOLOGY OFFICER, OPENAI

    Mr. Brockman. Chairwoman Comstock, Chairman Weber, Ranking 
Member Lipinski, Ranking Member Veasey, members of both 
subcommittees, thank you for having me today to deliver 
testimony.
    I'm Greg Brockman, Cofounder of OpenAI, a San Francisco-
based nonprofit with a mission to ensure that artificial 
general intelligence, which we define as systems--the highly 
autonomous systems that outperform humans at most economically 
valuable work--benefits all of humanity.
    Now, I'm here to tell you about the generality of modern 
AI, why AGI might actually be in reach sooner than commonly 
expected, and what action policymakers can take today.
    So, first, what's OpenAI? We're a research company with one 
of the world's most advanced AI research and development teams. 
Yesterday, we announced major progress towards a milestone that 
we, Alphabet's subsidiary DeepMind, and Facebook have 
separately been trying to reach, which is solving complex 
strategy games which start to capture many aspects of the real 
world that were just not seen in board games like chess or Go.
    We built a system called OpenAI Five, which learned to 
devise long-term plans and navigate scenarios far too complex 
to be programmed in by a human in order to solve a massively 
popular competitive game called Dota 2.
    Now, in the past, AI-like technology was written by humans 
in order to solve one specific problem at a time. It was not 
capable of adapting to solve new problems. Today's AI, it's all 
based on one core technique, which is the artificial neural 
network, a single simple idea that, as it's run on faster 
computers, is proving to match a surprising amount of human 
capability. And this was in fact something that was shown in 
part by my fellow witnesses Dr. Li's work in image recognition. 
And artificial neural networks can be trained to perform speech 
recognition or computer vision. It just depends on the data 
that they're shown.
    Now, further along the spectrum of generality is AGI. 
Rather than being developed for any one use case, AGI would be 
developed for a wide range of important tasks, and AGI would 
also be useful for noncommercial applications, including 
thinking through complex international disputes or city 
planning.
    Now, people have been talking about AGI for decades, and so 
how should we think about the timeline? Well, all AI systems, 
they're built on three foundations. That's data, computational 
power, and algorithms. Next-generation AI systems are already 
starting to rely less on conventional data sets where a human 
has provided the right answer. For example, one of our recent 
neural networks learned by reading 7,000 books.
    We also recently released a study showing that the amount 
of computation powering the largest AI training runs has been 
doubling every 3-1/2 months since 2012. That's a total increase 
of 300,000 times. And we expect this to continue for the next 
five years using only today's proven hardware technologies and 
not assuming any breakthroughs like quantum or optical.
    Now, to put that in perspective, that's like if your phone 
battery, which today lasts for a day, started to last for 800 
years and then, five years later, started to last for 100 
million years. It's this torrent of compute, this tsunami of 
compute. We've never seen anything like this. And so the open 
question is will this massive increase in combinational power, 
combined with near-term improvements in algorithmic 
understanding, be enough to develop AGI? We don't know the 
answer to this question today, but given the rapid progress 
that we are seeing, we can't confidently rule it out.
    And so now what should we be thinking about today? What can 
policymakers be doing today? And so, you know, the first thing 
to recognize is the core danger of AGI is that it has 
fundamentally the potential to cause rapid change whether 
that's through machines pursuing goals that are mis-specified 
by their operator, whether it's through malicious humans 
subverting deployed systems, or whether it's an economy that 
grows in an out-of-control way for its own sake rather than in 
order to improve human lives.
    Now, we spent two years. worth of policy research to create 
the OpenAI Charter, which in fact is a document I have right 
here in front of me. This contains three sections defining our 
views on safe and responsible AGI development. So that's--one 
is leaving time for safety and in particular refusing a race to 
the bottom on safety in order to reach AGI first. The second is 
to ensure that people at large rather than any one small group 
receive the benefits of this transformative technology. And the 
third is working together as a community in order to solve 
safety and policy challenges.
    Now, our primary recommendation to policymakers is to start 
measuring progress in this field. We need to understand how 
fast the field is moving, what capabilities are likely to 
arrive when in order to successfully plan for AGI challenges. 
That moves towards forecasts rather than intuition. Measurement 
is also a place where international coordination is actually 
valuable, and this is important if we want to spread safety and 
ethical standards globally.
    So thank you for your time, and I look forward to 
questions.
    [The prepared statement of Mr. Brockman follows:]

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   Chairwoman Comstock. Thank you. And we now recognize Dr. Li.

                  TESTIMONY OF DR. FEI-FEI LI,

        CHAIRPERSON OF THE BOARD AND CO-FOUNDER, AI4ALL

    Dr. Li. Thank you for the invitation, Congresswomen and 
Congressmen. My name is Fei-Fei Li. I'm here today as the 
Cofounder and Chairperson of AI4ALL, a national nonprofit 
organization focusing on bringing hands-on experience in AI 
research to high school students that have been traditionally 
underrepresented in the field of--in the STEM fields such as 
girls, people of color, and members of low-income communities. 
Our program began at Stanford University in 2015. This year, 
AI4ALL are expanded across North America to six university 
campuses.
    I often like to share with my students that there's nothing 
artificial about artificial intelligence. It's inspired by 
people, it's created by people, and, most importantly, it has 
an impact on people. It's a powerful tool we're only just 
beginning to understand, and that's a profound responsibility.
    I'm here today because the time has come to have an 
informed public conversation about that responsibility. With 
proper guidance, AI will make life better, but without it, it 
stands to widen the wealth divide even further, making 
technology even more exclusive, and reinforce biases we've 
spent generations trying to overcome. This will be an ethical, 
philosophical, and humanistic challenge, and it will require a 
diverse community of contributors. It's an approach I call 
human-centered AI. It's made of three pillars that I believe 
will help ensure AI plays a positive role in the world.
    The first is that the next generation of AI technology must 
reflect more of the qualities that make us human such as a 
deeper understanding of the context we rely on to make sense of 
the world. Progress on this front will make AI much better at 
understanding our needs but will require a deeper relationship 
between AI and fields like neuroscience, cognitive science, and 
the behavior sciences.
    The second is the emphasis on enhancing and augmenting 
human skills, not replacing them. Machines are unlikely to 
replace nurses and doctors, for example, but machine learning 
assistive diagnosis will help their job tremendously. Similar 
opportunities to intelligently augment human capabilities 
abound from health care to education, from manufacturing to 
agriculture.
    Finally, AI must be guided by a concern for its impact. We 
must address challenges of machine biases, security, privacy, 
as well as at the society level. Now is the time to prepare for 
the effect of AI on laws, ethics, and even culture.
    To put these ideas in practice, governments, academia, and 
industry will have to work together. This will require better 
understanding of AI in all three branches of government. AI is 
simply too important to be owned by private interests alone, 
and publicly funded research and education can provide a more 
transparent foundation for its development.
    Next, academia has a unique opportunity to elevate our 
understanding and development of this technology. Universities 
are a perfect environment for studying its effect on our world, 
as well as supporting cross-disciplinary next-generation AI 
research.
    Finally, businesses must develop a better balance between 
their responsibility to shareholders and their obligations to 
their users. Commercial AI products have the potential to 
change the world rapidly, and the time has come to complement 
this ambition with ethical, socially conscious policies.
    Human-centered AI means keeping humans at the heart of this 
technology's development. Unfortunately, lack of diverse 
representation remains a crisis in AI. Women hold a fraction of 
high-tech positions, even fewer at the executive level, and 
this is even worse for people of color. We have good reasons to 
worry about bias in our algorithms. A lack of diversity among 
the people developing these algorithms will be among its 
primary causes. One of my favorite quotes comes from technology 
ethicist Shannon Vallor, who says that ``There's no independent 
machine values. Machine values are human values.''
    However autonomous our technology becomes, its impact on 
the world will always be our responsibility. With the human-
centered approach, we can make sure it's an impact we'll be 
proud of. Thank you.
    [The prepared statement of Dr. Li follows:]
    
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    Chairwoman Comstock. Thank you. And I now recognize myself 
for five minutes for questions.
    Dr. Li, there's a generally accepted potential for AI-
enabled teaching to a minimum to provide a backup for 
traditional classroom education. So as AI technology advances, 
it seems reasonable to assume that your traditional education, 
vocational training, homeschooling, and even college coursework 
will need to change and adapt. Could you maybe comment about 
how education in general and for specific groups and 
individuals might be transformed by AI and how we can make that 
positive and really sort of have more of a democratization of 
education, particularly higher education and in STEM and in 
science?
    Dr. Li. Thank you for the question. Of course, I feel 
passionate about education. So I want to address this question 
in--from two dimensions. One is how could we improve the 
education of AI and STEM in general to more students and 
general community? Second is what can AI as a technology do to 
help education itself?
    On the first dimension, as evidenced by our work in the 
AI4ALL, we really believe that it's simultaneously a crisis and 
an important opportunity that we involve more people in the 
development of AI technology. AI represents--humanity has never 
created a technology so similar or trying to resemble who we 
are, and we need AI to--we need technologists and leaders of 
tomorrow to represent this technology.
    So, personally, I think we need to democratize AI's 
education to reach out to more students of color, girls, women 
from traditionally underrepresented minority groups. At AI4ALL 
for the past four years, we've already created an alumni 
population of more than 100 students, and through their own 
community and route outreach effort, we have touched the lives 
of more than 1,400 youth ranging from middle schoolers to high 
schoolers in disseminating this AI knowledge, and we need more 
of that in higher education.
    The second dimension that I want to answer your question is 
AI as a technology itself can help improve education itself. In 
the machine learning community, I'm sure, Greg, you also agree 
with me that there is an increasing recognition of the 
opportunity for lifelong learning using technology as an 
assistive technology. I have colleagues at Stanford who focus 
on research and reinforcement learning and education of how to 
bring more technological assistance into the teaching and 
territorialization of education itself, and I think this could 
become a huge tool, as I was saying, to augment human teachers 
and human educators to--so that our knowledge can reach to more 
students and wider community.
    Chairwoman Comstock. Excellent. And for other witnesses, 
could you maybe comment on how academic institutions and 
industry could work with government on AI?
    Mr. Brockman. All right. So, you know, for--OpenAI's 
recommendation is really about starting with measurement, 
right, to really start to understand what's happening in the 
field. I think it's really about, for example, the study that 
we did showing the 300,000 times increase. We need more of 
that. We need to understand where things are going, where we 
are. I think the government is uniquely positioned to set some 
of the goalposts as well, and we've been pretty encouraged by 
seeing some of the work that is happening at GAO and also DIUx 
has had some success with us. So we think it's really about 
starting a low touch way for the dialogue to start happening 
because I think right now the dialogue is not happening to the 
extent that it should.
    Dr. Persons. All right. Thank you for the question. I do 
think that, as the committees have pointed out, this is a 
whole-of-society issue. It's going to be government in 
partnership with the private sector, with academia to look at 
things. So I think there is room for thought about how to learn 
by doing, creating internships and ways to try and solve real-
world problems so that you have a mix of the classroom 
experience, as well as making and building--you'll fail a lot 
of course with these things but learning in a safe environment 
and then being able to grow expertise in that way.
    Chairwoman Comstock. Thank you. And, Dr. Li, did you have 
anything you wanted to add to that also? Okay. Well, thank you.
    And I now will recognize Mr. Lipinski for five minutes.
    Mr. Lipinski. Thank you. This is a fascinating topic. And 
there's--I want to try to move through some things quickly, but 
I'll get some good answers here.
    It seems to me that, Mr. Brockman, you have a different 
view of how--of AGI, the possibilities of AGI and how quickly 
it can come, then, the GAO report. Is there a reason for this? 
Is there something you think that GAO is missing? And if Dr. 
Persons could respond to that.
    Mr. Brockman. So I don't know if I can comment directly on 
the report just not being familiar enough with all the details 
in there, but I can certainly comment on our perspective on AGI 
and its possibility. And a lot of it really comes down to, 
rather than--you know, I think that there's been a lot of more 
emotion or intuition-based argument. And to your opening 
remarks, you know, I think that science-based reasoning in 
order to project what's happening in this field is extremely 
important, and that something that we've spent quite a lot of 
effort on since starting OpenAI almost three years ago.
    And so looking at the barriers to progress as compute data 
algorithms, data is something that's changing very rapidly in 
terms of what data we can use, the computation, the power there 
is growing at a rate that we've just never seen over the course 
of this decade. We're going to be talking, you know, I think 
about ten orders of magnitude, and that something where, if you 
were to compare that to the typical growth of compute, 
something like Moore's law, that the--over the period where we 
saw 300,000X increase in the past six years, we would've only 
seen 12X, right? That's a huge gap, and this is somewhere where 
we're sort of being projected into the future a lot faster than 
people realize.
    Now, it doesn't mean that it's going to happen soon. It 
means that we can't rule it out. It means that for the next 
five years, as long as this hardware growth is happening, we're 
in a fog and it's hard to make confident projections. And so my 
position is that we can't rule it out. We know that this is--
you know, we're talking about a technological revolution on the 
scale of the agricultural revolution, something that could be 
so beneficial to everyone in this world. And if we aren't 
careful in terms of thinking ahead and trying to be prepared, 
it really could be caught unaware.
    Mr. Lipinski. And thank you. Dr. Persons, do you have a 
response on that?
    Dr. Persons. Sure. I think--and with all respect for our 
Silicon Valley innovators who are upstarts and challenge the 
status quo, I think it's great that we have this system. The 
key thing that we're seeing is the convergence of these 
technologies that was mentioned by my panelists of the 
exponential power in computing, the ubiquitous nature of data, 
the sophistication of algorithms are all coming in.
    But that said, many folks in the community are mildly 
skeptical about the rate at which general AI may come in this 
area because--for several reasons. One is just the way that we 
think about the problem now, the super complexity that is 
manifest in addressing the various challenges. You're looking 
at large data sets and looking at all the facets of them. It's 
much easier to say than to do.
    And again, I think a lot of the--as you pointed out, the 
driving force here is the concern about general AI and taking 
over the world kind of thing, and it's just much harder to 
mimic human intelligence, especially in an environment where 
intelligence isn't even really defined or understood.
    And I think, as Dr. Li pointed out, a lot of this is really 
about augmentation. That's something else we heard from our 
experts. It wasn't a replacement of humans; it was a how can we 
become better humans, more functional humans in doing these 
things? So a lot of it just gets down to the----
    Mr. Lipinski. Let me--because I have a short time, sorry.
    Dr. Persons. Thank you.
    Mr. Lipinski. I just want to throw out quickly, the--there 
have been very different--vastly different opinions and--about 
the replacement of jobs and the disappearance of jobs and what 
the impact's going to be. Mr. Brockman, what do you think the 
impact will be?
    Mr. Brockman. So I think that with new technologies in the 
short term we always overestimate the degree to which they can 
make rapid change, but I think in the long-term that they do, I 
think technology is change in that we've seen with things like 
the internet, that there's been a lot of job displacement, both 
creation and destruction. And I think AI will be no different. 
I think the question of exactly which jobs and when I think we 
don't have enough information yet, and I think that that's 
where measurement really starts to come in. So we view it as an 
open question and a very important one.
    Dr. Persons. And, sir, if I can just say as a bottom line, 
nobody really knows the impact on this, and of course our 
experts are saying to know more we might need to be able to 
encourage--let's--for example, our Bureau of Labor Statistics, 
a data-type agency that--out of the federal government to help 
provide more data or different data or things to help try and 
answer the question of what is the impact as this technology 
continues to unfurl.
    That said, there's also a history of--when you--it goes 
back and attributed to Ned Ludd in the era of British 
industrialization and the concern of destroying the machines 
for the concern about loss of jobs, and yet--and many times 
throughout history, it's happened in an array of technologies 
where net jobs actually increased. It just--they were more 
sophisticated jobs. They were toward higher value creation and 
more productivity. So there is hope with this technology as 
well.
    Mr. Lipinski. And if the Chairwoman will allow, I want to 
hear from Dr. Li.
    Dr. Li. I just want to say that technology inevitably 
throughout human civilization has an impact to change the 
landscape of jobs, but it's really, really critical, like my 
fellow panelists said, that we need to invest in the research 
of how to assess this change. It's not a simple picture of 
replacement, especially when this technology has a much greater 
potential and power to augment it.
    I just spent days in the hospital ICU with my mother in the 
past couple of weeks, and--with my own health care and AI 
research, you recognize that a nurse in a single shift is doing 
hundreds of different tasks in our ICU unit where they're 
fighting for life and death for our patients, and these are not 
a simple question of replacing jobs but creating better 
technology to assist them and to make their jobs better and 
make the lives better for everyone. And that's what I hope we 
focus on, using this technology.
    Mr. Lipinski. Thank you.
    Chairwoman Comstock. Thank you, Dr. Li. That's a wonderful 
example of really vividly explaining to us how that can be used 
because certainly, as we're an aging population in this 
country, that's a challenge we're all facing. And so the 
quality of life and improvement in each of those employees and 
nurses being able to do a better job, thank you for outlining 
that.
    I now recognize Mr. Weber.
    Mr. Weber. Thank you, Madam Chair.
    Dr. Li, is your mom okay? We hope that she is and pray and 
hope that she is okay.
    Dr. Li. Thank you. I'm here. That means she's better.
    Mr. Weber. Okay. Otherwise, we were going to be missing two 
witnesses. Good.
    Dr. Li. She's watching me right now.
    Mr. Weber. Well, good.
    Chairwoman Comstock. Hi, Mom. She's doing a great job.
    Mr. Weber. You're doing excellent. She's a proud mom, and 
that's some good medicine in and of itself right there.
    Dr. Li. Thank you.
    Mr. Weber. So we're glad for that.
    Dr. Brockman, you in your statement say that your mission 
was to actually make sure that artificial intelligence 
benefited people and was better for the most economically 
valuable work. Do you remember that?
    Mr. Brockman. So are----
    Mr. Weber. It's in your written statement.
    Mr. Brockman. That's right. So the--our definition of what 
AGI will be, whether created by us or anyone else but just the 
milestone is a system that can outperform humans and 
economically valuable work.
    Mr. Weber. Okay. Well, let me read it to you real quick. 
``I'm Greg Brockman, Cofounder, a nonprofit development 
organization. Our mission is to ensure that artificial general 
intelligence, by which we mean highly autonomous systems that 
outperform humans at,'' quote, ``most economically valuable 
work,'' end quote, ``benefits all humanity.'' How would you 
define most economically valuable work?
    Mr. Brockman. So I think that, again--and, first of all, I 
just--you know, the question of--you know, AGI is something 
that the whole field has been working towards for--you know, 
really since the beginning of the field 50 years ago, and so 
the question of how to define it I think is something that is 
not entirely agreed-upon, that our definition is this--and when 
we think of it, we think of--you think about things like 
starting companies or very high intellectual work like that----
    Mr. Weber. Right.
    Mr. Brockman. --and, you know, also to things like going in 
cleaning up disaster sites or things that humans would be 
unable to do very well today.
    Mr. Weber. Okay. Well, I noticed that in your disagreement 
that Congressman Lipinski referred to with the report, and you 
call them Silicon Valley upstarts. At least you didn't call 
them young upstarts, so that's an advantage. Thank you for 
doing that. But you're literally looking at a new industry 
that, even though it's shifted--bless you--even though the 
shift is going to be changing, you're actually creating jobs 
for another industry.
    And going back to Dr. Li's example with her mom in the IU 
talking about much the nurses do, how do you train for those 
jobs if it's moving as fast as you think it is?
    Mr. Brockman. Yes, and so, you know, one thing I think is 
also very important is that I don't think we have much ability 
to change the timeline to this technology. I think that there 
are a lot of stakeholders, there are a lot of different pieces 
of the ecosystem. And that--what we do is we step back and we 
look at the trends and we say what's going to be possible when. 
And so I think that the question of how to train--and again, 
that's going to be something--we're not the only ones that are 
going to have to help answer that question.
    But I think that the place to start, it really comes back 
to measurement, right? If we don't know what's coming, if we 
can't project well, then we're going to be taken by surprise. 
And so, you know, I think that there are going to be lots of 
jobs and already have been created jobs that are surprising in 
terms of--you think about with autonomous vehicles, that we 
need to label all this data, we need to make sure that the 
systems are doing what we expect, and that all of that--that 
there's going to be humans that are going to help make these 
systems----
    Mr. Weber. But we would all agree, I hope--and this 
question is for all three panelists--all three witnesses, that 
the jobs they're going to create are well worth the 
transformation into all of that technology.
    Dr. Persons, would you agree with that?
    Dr. Persons. I would agree to that. I'll--let me give you a 
quick example if I may. Speaking with a former Secretary of 
Transportation recently, just a simple example of tollbooth 
collectors, we have now a system where you get the E-ZPass, you 
drive through, and you have less of a workforce there that 
could have had an impact at that time for short period on the 
number or loss of jobs for tollbooth collectors, and yet it 
freed them up. It enabled them to perhaps do other things that 
were needed and large problems.
    Mr. Weber. Okay. And, Mr. Brockman, you were shaking your 
head. You would agree with that statement?
    Mr. Brockman. Absolutely. I think that the purpose of 
technology and improving----
    Mr. Weber. Sure.
    Mr. Brockman. --it is to improve people's lives.
    Mr. Weber. So, Dr. Li, I see you shaking your head, too?
    Dr. Li. Yes, absolutely. In addition to the example Mr. 
Persons provided, I think deeply about the jobs that are 
currently dangerous and harmful for humans from fighting fires 
to search and rescue to, you know, natural disaster recovery. 
Not only we shouldn't put humans in harm's way if we can avoid 
it, but also we don't have enough help in these situations, and 
they are--this is where technology should be of tremendous 
help.
    Mr. Weber. Very quickly, I'm out of time, just yes or no. 
If we lose dominance in AI, that puts us in a really bad spot 
in worldwide competitiveness, would you agree?
    Dr. Persons. Yes.
    Mr. Brockman. Yes.
    Mr. Weber. Yes. Thank you.
    Dr. Li. Yes.
    Mr. Weber. Madam Chair, I yield back.
    Chairwoman Comstock. Thank you. Good question.
    Now, I recognize Mr. Veasey for five minutes.
    Mr. Veasey. Thank you, Madam Chair.
    We have heard about already from your testimony some of the 
advantages of AI and how it can help humankind, how it can help 
advance us as a nation and a country. But, as you know, there 
are people also that have concerns about AI. There's been a lot 
of sort of doomsday-like comparisons about AI and what the 
future of AI can actually mean.
    To what extent do you think this scenario, this sort of, 
you know, worst-case scenario that a lot of people have pointed 
out about AI is actually something that we should be concerned 
about? And if there is a legitimate concern, what can we do to 
help establish a more ethical, you know, responsible way to 
develop AI? And this is for anybody on the panel to answer.
    Mr. Brockman. So I think thinking about artificial general 
intelligence today is a little bit like thinking about the 
internet in maybe the late '50s, right? If someone was to 
describe to you what the internet was going to be, how it would 
affect the world, and the fact that all these weird things were 
going to start happening, you're going to have this thing 
called Uber which you're going to be able to--you'd just--you'd 
be very confused. It'd be very hard to understand what that 
would look like and the fact that, oh, we forget to put 
security in there and that we'd be paying for that for, you 
know, 30 years' worth of trying to fix things. And now imagine 
that that whole story, which played out over really the course 
of the past 60, almost 70 years now was going to play out in a 
much more compressed timescale.
    And so that's the perspective that I have when it comes to 
artificial general intelligence is the fact that it can cause 
this rapid change and it's already hard for us to cope with the 
changes that technology brings. And so the question of is it 
going to be malicious actors, is it going to be that the 
technology itself just wasn't built in a safe way, or is it 
just that the deployment that who owns it and the values that 
it's given aren't something that we're all very happy with? All 
of those I think are real risks, and again, that's something 
that we want to start thinking about today.
    Dr. Persons. Thank you, sir. So I agree with that. I think 
the key thing is being clear-eyed about what the risks actually 
are and not necessarily being driven by the entertaining and 
yet this science-fiction-type narrative sometimes on these 
things projecting or going to extremes and assuming far more 
than where we actually are in the technology.
    So it's--there are risks. It's understanding the risks as 
they are, and there are always contextual risks. Risks in 
automated vehicles are going to be different than risks in this 
technology in financial services, let's say. So it's really 
working, again, symbiotically with the community of practice 
and identify what are the things there? What are the 
opportunities? And there's going to be opportunities. And then 
what undesirable things do we want to focus on and then 
optimize from there on on how to deal with them. Thank you.
    Mr. Veasey. Mr. Brockman, in your testimony you had 
referenced a report outlining some malicious actors in this 
area. Could you sort of elaborate on some of your findings in 
these areas?
    Mr. Brockman. That's right. So OpenAI was a collaborator on 
this research report projecting not necessarily today what 
people are doing but looking forward what are some of the 
malicious activities that people could use AI for. And so that 
report--let's see. Yes, I think maybe the most important things 
here you start thinking about a lot of things around 
information, privacy, the question of how we actually ensure 
that these systems do what the operator intends, despite 
potential hacking. You think about autonomous systems that are 
taking action on behalf of humans that are subverted and 
whether, again, it's--you know, that this report focuses on 
active action. You think about autonomous vehicles and if a 
human hacker can go and take control of a fleet of those, some 
of the bad things that could happen.
    And so, you know, I think that this report should really be 
viewed as we need to be thinking about these things today 
before these are a problem because a lot of these systems are 
going to be deployed in a large-scale way, and if you're able 
to subvert them, then, you know, the--all of the problems that 
we've seen to date are going to start having a very different 
flavor where it's not just privacy anymore; it's also systems 
that are deployed in the real world that are actually able to 
affect our own well-being.
    Mr. Veasey. Thank you. Madam Chair, I yield back.
    Chairwoman Comstock. Thank you. And I now recognize Mr. 
Rohrabacher.
    Mr. Rohrabacher. Thank you very much, Madam Chairman.
    This, as in all advances in technology, can be seen as the 
great hope for making things better, or the new idea that there 
might be new dangers involved, or the new technologies will 
help certain peoples but be very damaging to others. And I 
think that where that fear would be most recognizable is in 
terms of employment and how in a free society people earn a 
living. And are we talking about here about the development of 
technology that will help get the tedious and remedial or the 
lower-skilled jobs that can be done by machine, or are we 
talking about the loss of employment by machines that are 
designed to really perform better than human beings perform in 
high-level jobs? What are we talking about here?
    Dr. Li. Okay. So I can--I'm still going to use health care 
as an example because I'm familiar with that area of research. 
So if you look at recent studies by McKinsey and other 
institutions on employment and AI, there is a recognition that 
we need to talk a little more nuanced than just entire job but 
the tasks under each job. The technology has the potential to 
change the nature of different tasks. Again, for example, take 
nurse--a job of a nurse as an example. It--no matter how 
rapidly we develop the technology and the most optimistic 
assessment, it's very hard to imagine the entire profession of 
nurse--nursing would be replaced, yet within the nursing jobs 
there are many opportunities that certain tasks can be assisted 
by AI technology.
    For example, a simple one that costs a lot of time and 
effort in nursing jobs is charting. Our nurses in our, again, 
ICU rooms, our patient rooms spend a lot of time typing and 
charting into a system, into a computer while that's time away 
from patients and other more critical care. So these are the 
kind of tasks under a bigger job description that we can hope 
to use technology to assist them and augment----
    Mr. Rohrabacher. So are we talking about robots here or a 
box that thinks and is able to make decisions for us? What are 
we talking about?
    Dr. Li. So AI technology is a technology of many different 
aspects. It's not just robot. In this particular case, for 
example, natural language, understanding the speech 
recognition, and possibly in the form of a voice assistant 
would help charting. But maybe delivering of simple tools on 
the factory floor will be in the form of a small simple 
delivery robot. So there are different forms of machines.
    Mr. Rohrabacher. I see. Well, there are many dangerous jobs 
that I could see that we'd prefer not having human life put at 
risk in order to accomplish the goal. And, for example, at 
nuclear power plants it would be a wondrous thing to have a 
robotic response to something that could cause great damage to 
the overall community but would kill somebody if they actually 
went in to try to solve a problem. And I understand that and 
also possibly with communicable diseases where people need to 
be treated but you're putting people at great risk for doing 
that.
    However, with that said, when people are seeking profit in 
a free and open society, I would hate to think that we're 
putting out of work people with lower skills, and we need the 
dignity of work and of earning your own way once we know now 
that when you take that away, it really has a major negative 
impact on people's lives.
    So I want to thank you all for giving us a better 
understanding of what we're facing on this, and let's hope that 
we can develop this technology in a way that helps the widest 
variety of people and not just perhaps a small group that will 
keep their jobs and keep the money. So thank you very much.
    Chairwoman Comstock. Thank you. And I now recognize Ms. 
Bonamici for five minutes.
    Ms. Bonamici. Thank you so much. Thank you to our 
witnesses.
    First, I want to note that our nation has some of the best 
scientists and researchers and engineers in the world, but 
without stronger investments in research and development, 
especially long-term foundational research, we risk falling 
behind, especially in this important area. I hope the research 
continues to acknowledge the socioeconomic aspects as well of 
integrating AI technologies.
    In my home State at the University of Oregon we have the 
Urbanism Next center. They're doing some great work bringing 
together interdisciplinary perspectives, including planning and 
architecture and engineering and urban design and public 
administration with public, private, and academic sectors to 
discuss how leveraging technology will shape the future of our 
communities. Their research has been talking about emerging 
technologies like autonomous vehicles and the implications for 
equity, health, the economy, and the environment and 
governance.
    Dr. Persons, can you discuss the value of establishing this 
type of partnership between industry, academia, and the private 
sector to help especially identify and address some of the 
consequences intended and unintended of AI as it becomes more 
prevalent? And I do have a couple more questions.
    Dr. Persons. Sure, I'll answer quickly. The short answer is 
yes. Our experts and what we're seeing is the value in public-
private partnerships because, again, it would be a mistake to 
look at this technology in sort of isolated stovepipes, and it 
would need to be an integrated approach to things, the federal 
government having its various roles but key--like your 
mentioning at University of Oregon, key academic and research 
questions. There's many, many things to research and questions 
to answer and then of course industry, which has an incredible 
amount of innovation and thinking and power to drive things 
forward.
    Ms. Bonamici. Terrific. Thank you. Dr. Li, I have a couple 
questions. You discuss the labor disruption, and I know that's 
brought up a couple of times and the need for retraining. We 
really have sort of a dual skills gap issue here because we 
want to make sure there are enough people who have the 
education needed for the AI industries, but we also are talking 
about the workers like you mentioned, the workers in tollbooths 
who will be displaced. But with the rapid development of 
technologies and the changes in this field, what knowledge and 
skills are the most important for a workforce capable of 
addressing the opportunities and the barriers to the 
development?
    I serve on the Education and Workforce Committee, and this 
is a really important issue is how do we educate people to be 
prepared for such rapid changes?
    Dr. Li. So AI is fundamentally a scientific and engineering 
discipline, and to--as an educator, I really believe in more 
investment in STEM education from early age on. We look at--in 
our experience at AI4ALL when we invited these high school 
students in the age of 14, 15, 16 to participate in AI 
research, their capabilities and potential just amazes me. We 
have high school students who have worked in my lab and won 
best-paper award at this country's best AI academic 
conferences. And so I believe passionately that STEM education 
is critical for the future for preparing AI workforce.
    Ms. Bonamici. Thank you. And as everyone on this committee 
knows, I always talk about STEAM because I'm a big believer in 
educating both halves of the brain, and students who have arts 
education tend to be more creative and innovative.
    Also, Dr. Li, in your testimony you talk about how AI 
engineers need to work with neuroscientists and cognitive 
scientists to help AI systems develop a more human feel. Now, I 
know Dr. Carbonell is not here today, but I noted in his 
testimony he wrote, ``AI is the ability to create machines who 
perform tasks normally associated with human intelligence.'' 
I'm sure that was an intentional choice to humanize the 
machine, but I wanted to ask you, Dr. Li, about--he's not here 
to explain, but I have no doubt that was intentional. In your 
testimony you talk about the laws that codify ethics. How is 
this going to be done? Can you go into more depth about how 
would these laws be done? Who would determine what is ethical? 
And would it be a combination of industry, government 
determining standards? How is--how are we going to set the 
stage for an ethical development of AI?
    Dr. Li. Yes, so thank you for the question. I think for 
technology as impactful as AI is to human society, it's 
critical that we have ethical guidelines. And different 
institutions from government to academia to industry will have 
to participate in this dialogue together and also by 
themselves.
    Ms. Bonamici. Are they already doing that, though? You said 
they'll have to but is somebody convening all of this to make 
sure that there are----
    Dr. Li. So there are efforts. I'm sure Greg can add to 
this. Industries in Silicon Valley we're seeing company 
starting to roll out AI ethical principles and responsible AI 
practices in academia. We see that ethicists and social 
scientists coming together with technologists holding seminars, 
symposiums, classes to discuss the ethical impact of AI. And 
hopefully, government will participate in this and support and 
invest and these kind of efforts.
    Ms. Bonamici. Thank you. I see my time is expired. Thank 
you, Madam Chair. I yield back. Oh, Mr. Chairman, thank you.
    Mr. Weber. [Presiding] I thank the gentlelady.
    And the gentlelady from Arizona is recognized for five 
minutes.
    Mrs. Lesko. Thank you, Mr. Chair.
    I want to thank the testifiers today, very interesting 
subject and something that kind of spurs the imagination about 
science fiction shows and those type of things.
    What countries are the major players in AI, and where does 
the United States rank in competition with them? And that's to 
any panelist or all panelists.
    Mr. Brockman. So, you know, today, I think that the United 
States actually ranks possibly top of the list. You know, I 
think there are lots of other countries that are investing very 
heavily. You know, China is investing heavily, lots of 
countries in Europe are investing heavily. The--you know, 
DeepMind is subsidiary of a U.S. company but located in London. 
And I think that, you know, it's very clear that AI is going to 
be something of global impact, and I just think the more that 
we can understand what's happening everywhere and figure out 
how we can coordinate on safety and ethics in particular, the 
better it's going to go.
    Dr. Persons. Yes, I--thank you for the question. I think 
wherever there is large amounts of computing, large amounts of 
data, and a strong desire to innovate and continue to develop 
again in this sort of fourth industrial revolution that we're 
moving on, then you--it drives toward certainly China and then 
our allies and colleagues in Western Europe and developed 
worlds. Thank you.
    Mrs. Lesko. And is there--did you want to answer?
    Mr. Brockman. Sorry----
    Mrs. Lesko. Go ahead.
    Mr. Brockman. If I could just add that, you know, the most 
important thing to continue to lead in the field, it's really 
about the talent. And right now, we're doing a great job of 
bringing all the talent in. At OpenAI we have a very wide mix 
of national backgrounds and origins, and I think as long as we 
can keep that up, that we'll be in very good shape.
    Mrs. Lesko. Thank you. And, Mr. Chair, I have one more 
question, and I think this has been asked in different ways 
before, but what steps are we guarding against, espionage from, 
let's say--you said China is involved in this and that's 
basically my question--espionage, hacking, those type of 
things. What guidelines are currently taking place, and who's 
preventing this? Is it the private companies themselves? Is 
government involved? Thank you.
    Mr. Brockman. So one thing that's a very atypical about 
this field is because it really grew out of an academic--very 
small number of academic labs that the overarching ethos in the 
field is actually to publish. And so all of the core research 
and development is actually being shared pretty widely. And so 
I think that as we're starting to build these more powerful 
systems and this is one of the parts of our charter that we 
need to start thinking about safety and keeping--you know, 
thinking about things that should not be shared, and so I think 
that this is a new muscle that's being built. It's right now 
kind of up to each company, and I think that that something 
that we're all starting to develop. But I think having a 
dialogue around what's okay to share and what things are kind 
of too powerful and should be kept private, that's just the 
dialogue that's starting now.
    Dr. Persons. And certainly IP or intellectual property 
protection is a critical issue. I think of one former Director 
of the National Security Agency mentioned that we're--at the 
time it was unprecedented theft of U.S. intellectual property 
at that time just because of the--it's the blessing and curse 
of the internet. It's a blessing it's this open and the curse 
is it's open. And so AI is going to I think be in that 
category.
    In terms of what's being done in terms of cybersecurity, it 
is something our experts pointed out and said it is an issue. 
As this Committee well knows, it's easier said than done, and 
who has jurisdictions in the U.S. federalist system about 
particularly a private company and protection of that, the role 
of the federal government versus the company itself in an era 
where, as I think Mr. Brockman has pointed out, is sort of the 
big-data era where data are the new oil, yet we want to be open 
at the same time so that we can innovate. So managing that 
dialectical tension is going to be a critical issue, and 
there's no easy answer.
    Mrs. Lesko. Thank you. Mr. Chair, I yield back.
    Mr. Weber. The Chair recognizes Ms. Esty for five minutes.
    Ms. Esty. Thank you, Mr. Chair, and I want to thank the 
witnesses for this extremely informative and important 
conversation that we're having here today.
    I hail from the State of Connecticut where we see a lot of 
innovation at UCONN, at Yale, at lots of spinoffs on the sort 
of narrow AI question. But I think for us really the issue is 
more about that general AI. And, Mr. Brockman, your discussion 
of the advances, which makes Moore's law look puny in 
comparison, is really where I want to take this conversation 
about, Dr. Li, your discussion, which I think is incredibly 
important, about diversity. We saw what happened to Lehman 
Brothers by not being diverse. I am extremely concerned about 
what the implications are for teaching a--as it were, if it's 
garbage in, it's going to be garbage out. If it's a very narrow 
set of parameters and thought patterns and life experiences 
that go into AI, we will get very narrow results out. So, 
first, I want to just talk--get your thoughts on that.
    And the second is on this broader ethical question. We've 
looked for many years--I remember back when I was a young 
lawyer working on bioethical issues. The Hastings Center got 
created to begin to look at these issues. This Committee has 
been grappling with CRISPR and the implications with CRISPR. I 
think about this being very similar, that AI has many similar 
implications for ethical input.
    So if you can opine on both of those questions and 
recognize we have got two minutes--three minutes left--about 
both the ethical--whether we need centers to really bring in 
ethicists, as well as technologists, and then the importance of 
diversity on the technology side so that we get the full range 
of human experience represented as we're exciting--our exciting 
new entry into this fourth revolution. Thanks.
    Dr. Li. Yes, in fact when--just now--thank you for asking 
that question. Just now when somebody is using the term 
doomsday scenario, to me I think if we wake up 20 years from 
now, whatever years, and we see the lack of diversity in our 
technology and leaders and practitioners, that would be my 
doomsday scenario. So it's so important and critical to have 
diversity for the following three reasons, like you mentioned. 
One is shared jobs that we're talking about. This is a 
technology that could have potential to create jobs and improve 
quality of life, and we need all talents to participate in 
that.
    Second is innovation and creativity like you mentioned in 
Connecticut and other places. We need that kind of broad talent 
to add in the force of AI development.
    And the third is really justice and moral values, that if 
we do not have this wide representation of humanity 
representing the development of this technology, we could have 
face-recognition algorithms that are more accurate in 
recognizing a male--white male faces. And these are--we could 
have dangers of out--biased algorithms making unfair loan 
application decisions. You know, there are many potential 
pitfalls of a technology that's biased and not diverse enough.
    Which brings us to this conversation and dialogue of ethics 
and ethical AI. You're right. Previous disciplines like nuclear 
physics, like biology have shown us the importance of this. I 
don't know if there is a single recipe, but I think the need 
for centers, institutions, boards, and government committees 
are all potential ways to create and open this dialogue. And 
again, we're starting to see that, but I think you're totally 
right. It's critical issues.
    Ms. Esty. Mr. Brockman?
    Mr. Brockman. If I may, so I agree completely with my 
fellow witness. So diversity is crucial to success here. So 
actually--so we have a program called OpenAI scholars where we 
brought in a number of people from underrepresented backgrounds 
into the field and provided mentorship and they're working on 
projects and spinning up. One thing that we found that I think 
is very encouraging is it's actually very easy to take people 
who do not have any AI or machine learning background and to 
make them into extremely productive first-class researchers and 
engineers very quickly. And that's, you know, one benefit of 
this technology being so new and nascent is that in some ways 
it--we're all discovering as we go along, too, so becoming an 
expert, there just isn't that high of a bar. But--so I think 
that everyone putting effort in to places where the expertise 
is, I think it's on them to make sure that they're also 
bringing in the rest of the world.
    On the ethical front, that's really core to my 
organization. That's the reason we exist, that we do think 
that, you know, for example, when it comes to the benefits of 
who owns this technology, who gets it--you know, where did the 
dollars go, we think it belongs to everyone. And so one of the 
reasons I'm here is because I think that this shouldn't be a 
decision that's made just in Silicon Valley. I don't think that 
the question of the ethics and how this is going to work should 
be in the hands solely of people like me. I think that it's 
really important to have a dialogue, and again, that something 
where, you know, I hope that that will be one of the outcomes 
of this hearing.
    Ms. Esty. Thank you very much.
    Mr. Weber. The gentleman now recognizes Mr. McNerney.
    Mr. McNerney. Well, I thank the Chair for holding this and 
the Ranking Member, and I thank the witnesses, really very 
interesting testimony and diverse in its own right.
    One of the things that I think that's important here is--
with this committee is how does the government react to AI? Do 
we need to create a specific agency? Does that agency report to 
Congress or to the Administration? Those sorts of things I 
think are very important.
    Dr. Brockman, you said--I think one of the most important 
things was that we need a measure of AI progress. Do you have a 
model or some description of what that would look like?
    Mr. Brockman. Yes, I do. Thank you for the question. And 
so, first of all, I don't think that we need to create new 
agencies for this. I think that existing agencies are well set 
up for this. And I was actually very encouraged to hear that 
people are talking about giving NIST a remit to think about 
these problems.
    Again, GAO and DIUx are already starting to work on this. 
For example, DIUx had a satellite imagery data set, hosted a 
public competition. The kind of thing that we think would be 
great for government to do as well is to have standardized 
environments where academics and private sector can test 
robotic approaches, setting up competitions towards specific 
problems that various agencies and departments want to be 
solved. All of those I think can be done without any new 
agency, and I think that that's something that you can both get 
benefits directly to the relevant agencies, also understand the 
field, and also start to build ties between private sector and 
public sector.
    Mr. McNerney. I'm one of the founders of the Grid 
Innovation Caucus. What are the most likely areas we'll see 
positive benefits to the grid, to electric grid stability and 
resiliency? Who would be the best to answer that? Mr. Persons?
    Dr. Persons. Sure. Thank you for the question. I think one 
of the ways--GAO has done a good deal of work on this issue, 
but it's just protection of the electrical grid in the 
cybersecurity dimension. So as one of our scenarios or profiles 
that we did in this report, what our experts and what folks are 
saying, and again based on the leadership of this Committee and 
the importance of cyber is that it's a--without which nothing--
AI is going to be a part of cyber moving forward, and so 
protection of the grid in the cyber dimension is there.
    Also, I think, as the Chairman mentioned earlier, the word 
optimization, so how we optimize things and how algorithms 
might be able to compute and find optimums faster and better 
than humans is an opportunity for grid management and 
production. Thank you.
    Mr. McNerney. So AI is also going to be used as a cyber 
weapon against infrastructures or potentially used as a weapon, 
is that right?
    Dr. Persons. There are concerns now when you look at a 
broad definition of AI and you look at bots now that are 
attacking networks and doing distributed--what are--DDOS or 
distributed denial of service attacks and things like that, 
that exists now. You could--unfortunately, in the black hat 
assumption you're going to assume that as AI becomes more 
sophisticated, the white hats, and so, too, unfortunately, the 
black hat side of things, the bad guys are going to also become 
more sophisticated. And so it's going--that's going to be the 
cat-and-mouse game I think moving forward.
    Mr. McNerney. Another question for you, Dr. Persons. In 
your testimony you mentioned that there's considerable 
uncertainty in the jobs impact of AI.
    Dr. Persons. Yes.
    Mr. McNerney. What would you do to improve that situation?
    Dr. Persons. Our experts were encouraging specific data 
collected on this. Again, we have important federal agencies 
like BLS, Bureau of Labor Statistics, that work on these 
issues, what's going on in the labor market, for example, and 
it may just be an update to what we collect, what questions we 
ask as a government, how we provide that data, which of course 
is very important to our understanding of unemployment metrics 
and so on.
    So there are economists that have thoughts about this. We 
had some input on that. There's no easy answer at this time, 
but the idea that there is an existing agency doing that sort 
of thing is there. The key question is how could we ask more or 
better questions on this particular issue on artificial 
systems?
    Mr. McNerney. Thank you. Dr. Li, you gave three conditions 
for progress in AI being positive. Do you see any acceptance or 
general wide acceptance of those conditions? How can we spread 
the word of that so that the industry is aware of them and the 
government is aware of them and that they follow those sorts of 
guidelines?
    Dr. Li. Thank you for asking. Yes, I would love to spread 
the word. So I think I do see that--the emergence of efforts in 
all three conditions. The first one is about more 
interdisciplinary approach to AI and ranging from universities 
to industry, we see the recognition of neuroscience, cognitive 
science to cross pollinate with AI research.
    I want to add we're all very excited by this technology, 
but as a scientist, I'm very humbled by how nascent the science 
is. It's only a science of 60 years old compared to 
traditionally classic science that's making human lives better 
every day, physics, chemistry, biology. There's a long, long 
way to go for AI to realize its full potential to help people. 
So that recognition really is important, and we need to get 
more research and cross-disciplinary research into that.
    Secondly is the augmenting human, and again, a lot of 
academic research as well as industry startup efforts are 
looking at assistive technology from disability to, you know, 
helping humans. And the third is what many of us focus on today 
is the social impact from studying it to having a dialogue to 
having--to working together through different industry and 
government agencies. So all three are the elements of human-
centered AI approach, and I see that happening more and more.
    Mr. McNerney. Thank you. I yield back.
    Mr. Weber. The Chair now recognizes the gentleman from New 
York. No. The Chair now recognizes the gentleman that's not 
from New York, Mr. Palmer.
    Mr. Palmer. Thank you, Mr. Chairman. I'd like to know if AI 
can help people who are geography-challenged.
    Mr. Weber. The gentleman's time has expired.
    Mr. Palmer. I request that that question and response be 
removed from the record.
    I do have some questions. In my district, we have the 
National Computer Forensics Institute, which deals with 
cybercrime, and what I'm wondering about is with the emergence 
of and evolution of AI. What are we putting in place because of 
the potential that that creates for committing crime and for 
solving crime? Dr. Persons, do you have any thoughts on that?
    Dr. Persons. Well, certainly in one of the areas we did--
thank you for the question. One of the areas we did look at in 
general was just criminal justice. So, I mean, just the risks 
that are there in terms of the social risks, making sure the 
scales are balanced exactly as they ought to be, that justice 
is blind, and so on was the focus of that.
    However, I think in terms of the criminal forensics, AI 
could be a tool that helps suss out what--you know, in a 
retrospective sense what happened. But again, it's an 
augmentation that's helping the forensic analyst who would know 
what things look like. And the algorithm would need--in the 
machine-learning sense of things would need to learn what the 
risks might be going forward so that you perhaps could identify 
things more proactively and perhaps in near or at real-time. So 
that's the opportunity for this. Again, AI is a tool and cyber 
was a key message we heard moving forward.
    Mr. Palmer. Any thoughts on that?
    Mr. Brockman. So, today, you know, we're already starting 
to see some of the security problems with the methods that 
we're creating, for example, that there's a new class of attack 
called adversarial examples where researchers are able to craft 
like a physical patch that you could print out and put on any 
object. They'll make a computer vision system think that it's 
whatever object you want it to be, so you could put that on a 
stop sign and confuse a self-driving car, for example. So these 
sorts of ways of subverting these powerful systems is something 
we're going to have to solve and going to have to work on, just 
like we've been working on computer security for more 
conventional systems.
    And I think that the way to think about if you could 
successfully build and deploy an AGI, what that would look 
like, in many ways it's kind of like the internet in terms of 
being very deeply integrated in people's lives but also having 
this increasing amount of autonomy and representation and 
taking action on people's behalf. And so you'll have kind of 
this question of how do you make sure, you know, first of all, 
that's something that could be great for security if these 
systems are well-built and have safety in their core and are 
very hard to subvert. But also if it's possible for people to 
hack them or to cause them to do things that are not aligned 
with the value of the operator, then I think that you can start 
having very large-scale disruption.
    Mr. Palmer. It also concerns me in the context of--it was 
announced a couple of weeks ago that the United States plans to 
form a space corps. We know that China has been very aggressive 
in militarizing space. If you have any thoughts on that 
discussion of how artificial intelligence will be used in 
regard to space. Communication systems that are highly 
vulnerable already, I think that there's some additional 
vulnerability that would be created. Any thoughts on that? And 
any one of the three of the panelists.
    Dr. Persons. Yes, sir. So in terms of the risk in space, 
obviously, one of the key concerns for AI is weaponization 
and--which I think is part of that and so much less the space 
domain or any other one. And so I know our Defense Department 
has key leadership thinking on this and working strategically 
on how do we operate in an environment where we have to assume 
there's going to be the adversary that might not operate in the 
ethical framework that we do and to defeat that, but there's no 
simple answer at this time other than our Defense Department is 
thinking about it and working on it.
    Mr. Palmer. And he's not here obviously to testify, but Dr. 
Carbonell's testimony, he made a statement that we need to 
produce or AI researchers, especially more U.S. citizen or 
permanent resident AI researchers. And I think that kind of 
plays into that issue of how do we deal with AI in space. 
That's one of the reasons why I've been pushing for a college 
program like an ROTC program to recruit people into the space 
corps in these areas, start identifying students when they're 
maybe even in junior high and scholarship them through college 
to get them into these positions. Any thoughts on that?
    Dr. Persons. I'll just answer quickly and just say I think, 
as Dr. Li has I think elegantly pointed out before, this is 
really an interdisciplinary thing. I think there's going to be 
a need for sort of the STEM, STEAM specialist that's 
particularly focused on this, but I think any particular 
vocation is going to be impacted in one way or the other, just 
like you could imagine rewinding a couple decades or a few 
decades--I'll date myself--but when the advent of the personal 
computer, the PC coming in and how that affected. Now, we walk 
into any vocation and somebody's using a PC or something like 
that and it's not unusual, but at the time you had to learn how 
to augment yourself or your task with that. And I think that 
will be the case.
    Mr. Palmer. Well, we're--if I may, Mr. Chairman--just to 
add this final thought that we've had to deal with some major 
hacks, federal government systems that are hacked, and what 
we're faced with, we're competing with the private sector for 
the best and brightest in terms of cybersecurity. We're going 
to find ourselves in the same situation with AI experts, the 
truly skilled people, and that's why I'm suggesting that we may 
need to start thinking about how do we recruit these people and 
get them as employees of the federal government? And that was 
my thoughts on setting up an ROTC-type program where we would 
recruit people in, we'd scholarship them, whether it's for 
cybersecurity or for AI and with a four- or five-year 
commitment to work for the federal government because there's 
going to be tremendous competition. And the federal government 
has a very difficult time competing for those type people.
    So with that, Mr. Chairman, I yield back.
    Mr. Weber. Now, the Chair recognizes the gentleman from New 
York.
    Mr. Tonko. It's okay. We're patient. I thank our respective 
Chairs and Ranking Members for today's very informative 
hearing.
    And welcome and thanks to our witnesses.
    I'm proud to represent New York's 20th Congressional 
District where our universities are leading the way in 
artificial intelligence research and education initiatives. 
SUNY Polytechnic Institute is currently the home of 
groundbreaking research developing neuromorphic circuits which 
could be used for deep learning such as pattern recognition but 
are also useful for AI or machine learning.
    In addition, the institute has established an ongoing 
research program on restive memory devices. Rensselaer 
Polytechnic Institute, RPI, is pushing the boundaries of 
artificial intelligence in a few different areas. In the 
healthcare front, RPI is focusing on improving people's lives 
and patient outcomes by collaborating with Albany Medical 
Center to improve the performance of their emergency department 
by using AI and analytics to reduce the recurrence of costly ER 
visits by patients. And RPI researchers are also collaborating 
with IBM to use the Watson computing platform to help people 
with prediabetes avoid developing the disease.
    In our fight to combat climate change and protect our 
environment, researchers at RPI and Earth and Environmental 
Science are working with computer science and machine learning 
researchers to apply cutting-edge AI to climate issues. In the 
education space, RPI is exploring new ways to use AI to improve 
teaching, as well as new approaches to teaching AI and data 
science to every student at RPI.
    With all that being said, there are tremendous universities 
across our country that are excelling in AI research and 
education. And what are some of the keys to helping AI 
institutions like them to excel? What do we need to do? What 
would be the most important? That's to any one of our 
panelists.
    Dr. Li. So thank you for asking this question. I think, 
just like we recognize AI really is such a widespread 
technology that I think one thing to recognize is that it is 
still so critical to support basic science research and 
education in our universities. This technology is far from 
being done. Of course, the industry is making tremendous 
investment and effort into AI, but it's a nascent science. It's 
a nascent technology. We have many unanswered questions, 
including the socially relevant AI, including AI for good, 
including AI for education, healthcare, and many other areas.
    So one of the biggest things I see would be investment into 
the basic science research into our universities and 
encouraging more students thinking in interdisciplinary terms, 
taking courses. You know, they can be STEM students, STEAM 
students. AI is not just for engineers and scientists; it could 
be for students with policymaking mind, for students with law 
interests, and so on. So I hope to see universities 
participating in this in a tremendous way, like many great 
schools in New York State.
    Mr. Tonko. Thank you. Dr. Persons or Mr. Brockman, either 
of you?
    Mr. Brockman. Sorry. I agree with Dr. Li but I also point 
out that I think it is also becoming increasingly hard to truly 
compete as an academic institution because if you look at 
what's happening, industry right now is actually doing 
fundamental research. It's very different from most scientific 
fields in that the salary disparity between what you can get at 
one of these industrial labs versus what you can get in 
academia, it's a very, very large.
    And there's a second piece, which is in order to do the 
research, you need access to massive computational resources. 
And, for example, the work that we just did with this, you 
know, game breakthrough that required basically a giant cluster 
of, you know, something around 10,000 machines. And that's 
something where in an academic setting it's not clear how you 
can get access to those resources. And so I think for the 
playing field to still be accessible, I think that there needs 
to be some story for how people in academic institutions can 
get access to that, and I think the question of, you know, 
where is the best research going to be done and where are the 
best people going to be, I think that's something that it's, 
you know, playing out right now in industry's favor, but it's 
not necessarily set in stone.
    Mr. Tonko. Thank you. Dr. Persons?
    Dr. Persons. Yes, sir. Thank you for the question. And I 
would just add to my fellow panelists the fact that our experts 
had said that real-world testbeds are important to this. You 
don't know what you don't know, so not only in addition to 
adding access to data but being able to test and do things, 
these--one thing for sure, and I learned, in fact, from OpenAI 
that a lot of the times these things come out with surprising 
results, and so that's the whole reason of creating safe 
environments to try things out and de-risk those technologies. 
And that's something that I think is going to be important to 
enable that base of research to have an avenue to perhaps move 
up the technology maturity scale possibly into the market and 
certainly hopefully to solve critical, complex, real-world 
problems.
    Mr. Tonko. Thank you. Very informative. Mr. Chair, I yield 
back.
    Mr. Weber. The Chair now recognizes the gentleman from 
Illinois.
    Mr. Foster. Thank you, Mr. Chairman. And thank you for 
coming to testify today.
    You know, I've been interested in artificial intelligence 
for quite a long time. Back in the 1990s working in particle 
physics we were using neural network classifiers to have a look 
at trying to classify particle physics interactions. And when I 
couldn't stand it during the government shutdown and not so 
long ago, I went and downloaded TensorFlow and worked through 
the--part of the tutorial on it.
    And, you know, the algorithms are not so different than 
what we were using back in the 1990s, but the computing power 
difference is breathtaking. And I very much resonated with your 
comments on the huge increase in dedicated computer power for 
deep learning and similar--and that is likely to be 
transformative, given the recent--and so that--you know, we 
have to think through that because even with no new brilliant 
ideas on algorithms, there's going to be a huge leap forward. 
So thank you for that. That's a key observation here.
    You know, in Congress I'm the co-Chair of the New Democrats 
Coalition on the Future of Work Task Force where we have been 
trying to think through what this means for the workplace of 
the future. And so I'd like to--if--Mr. Chairman, I'd like to 
submit for the record a white paper entitled ``Closing the 
Skills and Opportunity gaps,'' without objection.
    Mr. Weber. Without objection, so ordered.
    [The information appears in Appendix II]
    Mr. Foster. Thank you. And I will be asking for the record 
if you could have a look at this and see if--you know, how--
what sort of coverage you think this document has for the near-
term policy responses because it's--you know, this is coming at 
us I think faster than a lot of people in politics really 
understand.
    And also, I will be asking for the record--I guess you may 
not have to respond right now--where the best sources of 
information on how quickly this will be coming at us. You know, 
there are conferences here and there, but you attend and your 
friends attend a lot of them. I'd be interested in where you 
think--you really come together to get the techno-experts, the 
economic experts, you know, the labor economists, people like 
that all in the same room. I think it's something we should be 
putting more effort into.
    On another track, I've been very involved in Congress in 
trying to resurrect something called the Office of Technology 
Assessment. You know, and what the J.O. did here is very good, 
which is to bring--we had a conference of the experts, and you 
brought in a good set of experts. And a year later now we are 
getting a report on this. And, you know, you need more 
bandwidth in Congress than that just, you know, on all 
technological issues, but this is a perfect example. A year-old 
group of experts and then AI, you know, is--those are opinions 
that are sort of dated a little bit, even a year in the past.
    And so the Office of Technology Assessment for decades 
provided immediate high-bandwidth advice to Congress on all 
sorts of technological issues. And so we're coming closer and 
closer every year in getting it refunded after it was defunded 
in the 1990s. And so I think--well, to ask you a question here, 
is there anyone on the panel who thinks that Congress has 
enough technological capacity as it currently stands to deal 
with issues like this?
    Dr. Persons. So----
    Mr. Weber. I can answer that.
    Mr. Foster. Yes--no, it's a huge problem, and it's been 
aggravated by the fact that people have decided in their wisdom 
to cut back on the size and salaries available for 
Congressional staff. One of my--the previous members who talked 
about--here talked about the difficulty the federal government 
will have in getting real professionals, top-of-the-line 
professionals in here, and, you know, we're seeing Members of 
Congress who are willing to do anything but give them the 
salaries that will be necessary to actually compete for those 
jobs.
    Let's see. I am now--let's see. Oh, Mr. Brockman, you had--
your--I would advocate everyone have a look at the reference 5 
in--which is your malicious use of AI, your--reference 5 in 
your testimony, which I spent--I stayed up way too late last 
night reading that, and it is real.
    Along the same lines, Members of Congress have access to 
the classified version of a National Academies of Science study 
on the implication of autonomous drones for--and this is 
something that I think, you know, has to be understood by the 
military. We're about to mark up a military authorization bill, 
an appropriations bill that is spending way too much money 
fighting the last war and not enough fighting the wars of the 
future.
    And then finally, Dr. Li, the--in the educational aspects 
of this, one thing I struggle for--I guess if you look through 
the bios of people who are the heroes of artificial 
intelligence, they tend to come from physics, math, places like 
that. And in theoretical physics or mathematics a huge fraction 
of the progress comes from a tiny fraction of people. It's just 
a historical truth. And I was wondering, is AI like that? Are 
they--you know, are there a small number of heroes that really 
do most of the work and everyone else sort of fills in the 
thing?
    Dr. Li. So, like I said, Dr. Foster, AI is a very nascent 
field, so even though it is collecting a lot of enthusiasm 
worldwide, societally, as a science, it's still very young. And 
as a young science, it starts from a few people.
    As a--I was also trained as a physics major, and I think 
about early days of Newtonian physics, and that was a smallish 
group of people as well. I mean, it's--it would be too much to 
compare directly, but what I really do want to say is that we--
maybe in the early, even pre-Newtonian days of AI, we are still 
developing this, so the number of people are still small.
    Having said that, there are many, many people who have 
contributed to AI. Their names might not have made it to the 
news, to the blogs, to the tweets, but these are the names 
that, as students and experts of this field, we remember them. 
And I want to say many of them are members of the 
underrepresented minority group. There are many women in the 
first generation of AI experts. So----
    Mr. Foster. Yes. And when I was----
    Dr. Li. --we need to hear more from them.
    Mr. Foster. --you know, two or three clicks down in the 
references cited by your testimony and you look at the papers 
there and the author lists, it's pretty clear that our 
dominance in AI is due to immigrants, okay? And, Dr. Li, I 
suspect you might not have come to this country under the 
conditions that are now being proposed by our President. And I 
won't ask you to answer that, but it's important when we talk 
about what it is that makes this country dominant in things 
like AI. It is immigrants, okay? And I'll just leave it at 
that, and I guess my time is up.
    Mr. Weber. I thank the gentleman. I thank the witnesses for 
their testimony and the Members for their questions. The record 
will remain open for two weeks for additional written comments 
and written questions from Members.
    The hearing is adjourned.
    [Whereupon, at 12:24 p.m., the Subcommittees were 
adjourned.]

                               Appendix I

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